WEEK 8: FUNCTIONAL PROGRAMMING

Monday, February 27th

Today we will…

  • Questions about Lab 7
  • Project Proposal – Data
  • Mini lecture on text material1
    • Review vectorization
    • The map() family
  • PA 8: The 12 Days of Christmas Starter Functions

Iteration

Repeating the same operation multiple times

Image source: https://bookdown.org/hneth/ds4psyl

Vectorized Functions

  • Operations in R are typically vectorized

  • By default, the functions operate on vectors of values rather than a single value.

x <- seq(from = -4, to = 12, by = 0.5)
x
 [1] -4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5  0.0  0.5  1.0  1.5  2.0  2.5  3.0
[16]  3.5  4.0  4.5  5.0  5.5  6.0  6.5  7.0  7.5  8.0  8.5  9.0  9.5 10.0 10.5
[31] 11.0 11.5 12.0
abs(x)
 [1]  4.0  3.5  3.0  2.5  2.0  1.5  1.0  0.5  0.0  0.5  1.0  1.5  2.0  2.5  3.0
[16]  3.5  4.0  4.5  5.0  5.5  6.0  6.5  7.0  7.5  8.0  8.5  9.0  9.5 10.0 10.5
[31] 11.0 11.5 12.0
x <- seq(from = -4, to = 12, by = 0.5)
for(i in length(x)){
  x[i] <- abs(x[i])
}

x
 [1] -4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5  0.0  0.5  1.0  1.5  2.0  2.5  3.0
[16]  3.5  4.0  4.5  5.0  5.5  6.0  6.5  7.0  7.5  8.0  8.5  9.0  9.5 10.0 10.5
[31] 11.0 11.5 12.0

Is every function vectorized? Short answer, no.

For example, if the function you’ve written makes use of if() statements, your function cannot operate on vectors.

pos_neg_zero <- function(x){
  stopifnot(is.numeric(x))
  
  if(x > 0){
    return("Greater than 0!")
  } else if (x < 0){
    return("Less than 0!")
  } else {
    return("Equal to 0!")
      }
}

x <- seq(from = -4, to = 4, by = 1)

pos_neg_zero(x)
Error in if (x > 0) {: the condition has length > 1

This error means that the if(x > 0) condition can only be checked for something of length 1.

result <- rep(NA, length(x))

for(i in 1:length(x)){
  result[i] <- pos_neg_zero(x[i])
}

result
[1] "Less than 0!"    "Less than 0!"    "Less than 0!"    "Less than 0!"   
[5] "Equal to 0!"     "Greater than 0!" "Greater than 0!" "Greater than 0!"
[9] "Greater than 0!"
pos_neg_zero <- function(x){
  stopifnot(is.numeric(x))
  
  state <- case_when(x > 0 ~ "Greater than 0!", 
                     x < 0 ~ "Less than 0!", 
                     TRUE ~ "Equal to 0!")
  return(state)
}

x <- seq(from = -4, to = 4, by = 1)
pos_neg_zero(x)
[1] "Less than 0!"    "Less than 0!"    "Less than 0!"    "Less than 0!"   
[5] "Equal to 0!"     "Greater than 0!" "Greater than 0!" "Greater than 0!"
[9] "Greater than 0!"

When can’t you vectorize your function?

Applying typeof() to a single variable tells us the datatype of that column.

typeof(penguins$species)
[1] "integer"

Applying typeof() to every variable in the data frame returns an object structure of the penguins data frame, not the datatype of its columns.

typeof(penguins)
[1] "list"

What can we do instead?

Your good CSC 101 friend, the for() loop

data_type <- rep(NA, length = ncol(penguins))

for(i in 1:ncol(penguins)){
  data_type[i] <- typeof(penguins[[i]])
}

# format table nicely
tibble(column = names(penguins), 
       type = data_type) %>% 
  pivot_wider(names_from = column, 
              values_from = type) %>% 
  knitr::kable()
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex year
integer integer double double integer integer integer integer

… but we don’t really need this in R because it is a functional programming language

Remember across()

We have continually called on the across() function to reduce repetition in our code, when performing the same operation(s) on multiple columns.

penguins %>% 
  summarise(
    across(
      .cols = everything(), 
      .fns = ~ sum(is.na(.x))
      )
    )
# A tibble: 1 × 8
  species island bill_length_mm bill_depth_mm flipper_leng…¹ body_…²   sex  year
    <int>  <int>          <int>         <int>          <int>   <int> <int> <int>
1       0      0              2             2              2       2    11     0
# … with abbreviated variable names ¹​flipper_length_mm, ²​body_mass_g

Ugh. but across() uses a for() loop “behind the scenes”

for (j in seq_fns) {
  fn <- fns[[j]]
  out[[k]] <- fn(col, ...)
  k <- k + 1L

Image source: https://bookdown.org/hneth/ds4psyl

Functional Programming

To understand computations in R, two slogans are helpful:

  • Everything that exists is an object.

  • Everything that happens is a function call.

John Chambers

Functional Programming

The big picture: Using functions as arguments to functions

Note

There are a slew of apply() functions you will likely come across, but we will instead focus on the purrr package and map() family of functions.

The best option: library(purrr)

The main goal of using purrr functions (instead of for loops) is to break common list manipulation challenges into smaller and independent pieces. This strategy involves two steps, each of which scales down the problem:

  1. Solving the problem for a single element of a list. Once we have solved that problem, purrr takes care of generalizing the solution to every element in the list.

  2. Breaking a complex problem down into smaller sub-problems that allow us to advance towards a solution. With purrr, we get many small pieces that we can compose together with the pipe (|>).

This scaling-down strategy makes it easier to solve new problems and to understand our solutions to old problems when we re-read older code.

Note

See the purrr cheatsheet

Introduction to map()

map iterates through each item in the list and applies a function.

map() family

map_xxx variants allow you to specify the type of output you want back

  • map() creates a list
  • map_chr() creates a character vector
  • map_lgl() creates an logical vector
  • map_int() creates a integer vector
  • map_dbl() creates a numeric vector
  • map2_chr()
  • map2_lgl()
  • map2_int()
  • map2_dbl()
  • pmap()

Example – map() + penguins

penguins |> 
  select(bill_length_mm:body_mass_g) |> 
  summarize(
    across(
      .cols = bill_length_mm:body_mass_g,
      .fns = ~ mean(.x, na.rm = TRUE)
      )
  )
# A tibble: 1 × 4
  bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
           <dbl>         <dbl>             <dbl>       <dbl>
1           43.9          17.2              201.       4202.
penguins |> 
  select(bill_length_mm:body_mass_g) |> 
  map_dbl(.f = ~ mean(.x, na.rm = TRUE))
   bill_length_mm     bill_depth_mm flipper_length_mm       body_mass_g 
         43.92193          17.15117         200.91520        4201.75439 
penguins |> 
  map_int(.f = ~ sum(is.na(.x)))
          species            island    bill_length_mm     bill_depth_mm 
                0                 0                 2                 2 
flipper_length_mm       body_mass_g               sex              year 
                2                 2                11                 0
  • Note the ~ and the .x, just like in across()!

R will return an error if the output is the wrong type

penguins |> 
  map_lgl(.f = ~ sum(is.na(.x)))
Error in `map_lgl()`:
ℹ In index: 3.
ℹ With name: bill_length_mm.
Caused by error:
! Can't coerce from an integer vector to a logical vector.

Example – map() + penguins

penguins |> 
  mutate(
    across(
      .cols = where(is.numeric), 
      .fns = scale
    )
  )
# A tibble: 344 × 8
   species island    bill_length_mm[,1] bill_dep…¹ flipp…² body_…³ sex   year[…⁴
   <fct>   <fct>                  <dbl>      <dbl>   <dbl>   <dbl> <fct>   <dbl>
 1 Adelie  Torgersen             -0.883      0.784  -1.42  -0.563  male    -1.26
 2 Adelie  Torgersen             -0.810      0.126  -1.06  -0.501  fema…   -1.26
 3 Adelie  Torgersen             -0.663      0.430  -0.421 -1.19   fema…   -1.26
 4 Adelie  Torgersen             NA         NA      NA     NA      <NA>    -1.26
 5 Adelie  Torgersen             -1.32       1.09   -0.563 -0.937  fema…   -1.26
 6 Adelie  Torgersen             -0.847      1.75   -0.776 -0.688  male    -1.26
 7 Adelie  Torgersen             -0.920      0.329  -1.42  -0.719  fema…   -1.26
 8 Adelie  Torgersen             -0.865      1.24   -0.421  0.590  male    -1.26
 9 Adelie  Torgersen             -1.80       0.480  -0.563 -0.906  <NA>    -1.26
10 Adelie  Torgersen             -0.352      1.54   -0.776  0.0602 <NA>    -1.26
# … with 334 more rows, and abbreviated variable names ¹​bill_depth_mm[,1],
#   ²​flipper_length_mm[,1], ³​body_mass_g[,1], ⁴​year[,1]
penguins |> 
  map_if(is.numeric, scale)
$species
  [1] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
  [8] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [15] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [22] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [29] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [36] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [43] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [50] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [57] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [64] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [71] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [78] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [85] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [92] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [99] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
[106] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
[113] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
[120] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
[127] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
[134] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
[141] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
[148] Adelie    Adelie    Adelie    Adelie    Adelie    Gentoo    Gentoo   
[155] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[162] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[169] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[176] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[183] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[190] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[197] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[204] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[211] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[218] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[225] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[232] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[239] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[246] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[253] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[260] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[267] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[274] Gentoo    Gentoo    Gentoo    Chinstrap Chinstrap Chinstrap Chinstrap
[281] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[288] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[295] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[302] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[309] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[316] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[323] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[330] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[337] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[344] Chinstrap
Levels: Adelie Chinstrap Gentoo

$island
  [1] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
  [8] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
 [15] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Biscoe   
 [22] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
 [29] Biscoe    Biscoe    Dream     Dream     Dream     Dream     Dream    
 [36] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
 [43] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
 [50] Dream     Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
 [57] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
 [64] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Torgersen Torgersen
 [71] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
 [78] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
 [85] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
 [92] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
 [99] Dream     Dream     Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[106] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[113] Biscoe    Biscoe    Biscoe    Biscoe    Torgersen Torgersen Torgersen
[120] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
[127] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Dream    
[134] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[141] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[148] Dream     Dream     Dream     Dream     Dream     Biscoe    Biscoe   
[155] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[162] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[169] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[176] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[183] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[190] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[197] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[204] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[211] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[218] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[225] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[232] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[239] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[246] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[253] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[260] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[267] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[274] Biscoe    Biscoe    Biscoe    Dream     Dream     Dream     Dream    
[281] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[288] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[295] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[302] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[309] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[316] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[323] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[330] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[337] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[344] Dream    
Levels: Biscoe Dream Torgersen

$bill_length_mm
              [,1]
  [1,] -0.88320467
  [2,] -0.80993901
  [3,] -0.66340769
  [4,]          NA
  [5,] -1.32279862
  [6,] -0.84657184
  [7,] -0.91983750
  [8,] -0.86488825
  [9,] -1.79902541
 [10,] -0.35202864
 [11,] -1.12131806
 [12,] -1.12131806
 [13,] -0.51687637
 [14,] -0.97478674
 [15,] -1.70744334
 [16,] -1.34111504
 [17,] -0.95647033
 [18,] -0.26044656
 [19,] -1.74407616
 [20,]  0.38062795
 [21,] -1.12131806
 [22,] -1.13963448
 [23,] -1.46932994
 [24,] -1.04805240
 [25,] -0.93815391
 [26,] -1.57922843
 [27,] -0.60845845
 [28,] -0.62677486
 [29,] -1.10300165
 [30,] -0.62677486
 [31,] -0.80993901
 [32,] -1.23121655
 [33,] -0.80993901
 [34,] -0.55350920
 [35,] -1.37774787
 [36,] -0.86488825
 [37,] -0.93815391
 [38,] -0.31539581
 [39,] -1.15795089
 [40,] -0.75498976
 [41,] -1.35943145
 [42,] -0.57182562
 [43,] -1.45101353
 [44,]  0.03261607
 [45,] -1.26784938
 [46,] -0.79162259
 [47,] -0.51687637
 [48,] -1.17626731
 [49,] -1.45101353
 [50,] -0.29707939
 [51,] -0.79162259
 [52,] -0.70004052
 [53,] -1.63417768
 [54,] -0.35202864
 [55,] -1.72575975
 [56,] -0.46192713
 [57,] -0.90152108
 [58,] -0.60845845
 [59,] -1.35943145
 [60,] -1.15795089
 [61,] -1.50596277
 [62,] -0.48024354
 [63,] -1.15795089
 [64,] -0.51687637
 [65,] -1.37774787
 [66,] -0.42529430
 [67,] -1.54259560
 [68,] -0.51687637
 [69,] -1.46932994
 [70,] -0.38866147
 [71,] -1.90892390
 [72,] -0.77330618
 [73,] -0.79162259
 [74,]  0.34399512
 [75,] -1.54259560
 [76,] -0.20549732
 [77,] -0.55350920
 [78,] -1.23121655
 [79,] -1.41438070
 [80,] -0.33371222
 [81,] -1.70744334
 [82,] -0.18718091
 [83,] -1.32279862
 [84,] -1.61586126
 [85,] -1.21290014
 [86,] -0.48024354
 [87,] -1.39606428
 [88,] -1.28616579
 [89,] -1.02973599
 [90,] -0.91983750
 [91,] -1.50596277
 [92,] -0.51687637
 [93,] -1.81734182
 [94,] -0.79162259
 [95,] -1.41438070
 [96,] -0.57182562
 [97,] -1.06636882
 [98,] -0.66340769
 [99,] -1.98218956
[100,] -0.13223166
[101,] -1.63417768
[102,] -0.53519279
[103,] -1.13963448
[104,] -1.12131806
[105,] -1.10300165
[106,] -0.77330618
[107,] -0.97478674
[108,] -1.04805240
[109,] -1.06636882
[110,] -0.13223166
[111,] -1.06636882
[112,]  0.30736229
[113,] -0.77330618
[114,] -0.31539581
[115,] -0.79162259
[116,] -0.22381374
[117,] -0.97478674
[118,] -1.21290014
[119,] -1.50596277
[120,] -0.51687637
[121,] -1.41438070
[122,] -1.13963448
[123,] -0.68172411
[124,] -0.46192713
[125,] -1.59754485
[126,] -0.60845845
[127,] -0.93815391
[128,] -0.44361071
[129,] -0.90152108
[130,]  0.03261607
[131,] -0.99310316
[132,] -0.15054808
[133,] -1.30448221
[134,] -1.17626731
[135,] -1.06636882
[136,] -0.51687637
[137,] -1.52427919
[138,] -0.68172411
[139,] -1.26784938
[140,] -0.77330618
[141,] -0.68172411
[142,] -0.60845845
[143,] -2.16535371
[144,] -0.59014203
[145,] -1.21290014
[146,] -0.90152108
[147,] -0.86488825
[148,] -1.34111504
[149,] -1.45101353
[150,] -1.12131806
[151,] -1.45101353
[152,] -0.44361071
[153,]  0.39894437
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[155,]  0.87517115
[156,]  1.11328455
[157,]  0.67369059
[158,]  0.47221003
[159,]  0.27072946
[160,]  0.50884286
[161,] -0.11391525
[162,]  0.52715927
[163,] -0.55350920
[164,]  0.93012040
[165,]  0.28904588
[166,]  0.82022191
[167,]  0.34399512
[168,]  0.98506964
[169,] -0.35202864
[170,]  0.96675323
[171,]  0.41726078
[172,]  0.87517115
[173,]  1.14991738
[174,]  0.21578022
[175,]  0.47221003
[176,]  0.43557720
[177,] -0.18718091
[178,]  0.39894437
[179,]  0.10588173
[180,]  0.71032342
[181,]  0.78358908
[182,]  1.11328455
[183,]  0.61874135
[184,] -0.20549732
[185,]  0.21578022
[186,]  2.87166037
[187,]  0.94843681
[188,]  0.82022191
[189,] -0.24213015
[190,]  0.08756532
[191,]  0.01429966
[192,]  0.87517115
[193,] -0.22381374
[194,]  1.04001889
[195,]  0.25241305
[196,]  1.04001889
[197,]  1.20486662
[198,] -0.05896600
[199,]  0.28904588
[200,]  1.20486662
[201,]  0.17914739
[202,]  0.23409663
[203,]  0.49052644
[204,]  0.83853832
[205,]  0.21578022
[206,]  1.13160096
[207,]  0.47221003
[208,]  0.19746381
[209,] -0.02233317
[210,]  0.28904588
[211,] -0.13223166
[212,]  1.18655021
[213,]  0.25241305
[214,]  0.41726078
[215,]  0.32567871
[216,]  1.90089038
[217,]  0.34399512
[218,]  1.07665172
[219,]  0.41726078
[220,]  1.02170247
[221,] -0.07728242
[222,]  1.24149945
[223,]  0.69200701
[224,]  0.45389361
[225,]  0.78358908
[226,]  0.47221003
[227,]  0.45389361
[228,]  0.85685474
[229,]  0.65537418
[230,]  1.31476511
[231,]  0.23409663
[232,]  0.23409663
[233,]  0.94843681
[234,]  1.57119492
[235,]  0.63705776
[236,]  1.11328455
[237,]  0.17914739
[238,]  1.25981586
[239,] -0.09559883
[240,]  1.35139794
[241,]  0.65537418
[242,]  1.49792926
[243,]  0.65537418
[244,]  1.51624567
[245,]  0.28904588
[246,]  1.02170247
[247,]  0.10588173
[248,]  1.25981586
[249,]  1.00338606
[250,]  0.54547569
[251,]  0.82022191
[252,]  1.31476511
[253,]  0.83853832
[254,]  2.19395302
[255,]  0.60042493
[256,]  0.94843681
[257,]  0.61874135
[258,]  0.52715927
[259,] -0.40697788
[260,]  1.73604265
[261,] -0.11391525
[262,]  0.76527266
[263,]  1.20486662
[264,]  1.07665172
[265,] -0.07728242
[266,]  1.38803077
[267,]  0.41726078
[268,]  2.04742170
[269,]  0.10588173
[270,]  0.89348757
[271,]  0.60042493
[272,]          NA
[273,]  0.52715927
[274,]  1.18655021
[275,]  0.23409663
[276,]  1.09496813
[277,]  0.47221003
[278,]  1.11328455
[279,]  1.35139794
[280,]  0.27072946
[281,]  1.60782775
[282,]  0.23409663
[283,]  0.39894437
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[290,]  1.47961284
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[293,]  1.16823379
[294,]  2.57859773
[295,]  0.45389361
[296,]  0.96675323
[297,] -0.27876298
[298,]  0.83853832
[299,] -0.13223166
[300,]  1.22318303
[301,]  0.50884286
[302,]  1.47961284
[303,]  1.20486662
[304,]  1.02170247
[305,]  0.45389361
[306,]  1.62614416
[307,] -0.55350920
[308,]  1.88257397
[309,] -0.26044656
[310,]  1.29644869
[311,]  1.05833530
[312,]  0.65537418
[313,]  0.67369059
[314,]  1.47961284
[315,]  0.54547569
[316,]  1.75435906
[317,]  0.93012040
[318,]  0.41726078
[319,]  1.27813228
[320,]  0.28904588
[321,]  1.27813228
[322,]  1.25981586
[323,]  1.13160096
[324,]  0.93012040
[325,]  1.38803077
[326,]  1.07665172
[327,]  0.76527266
[328,]  1.36971435
[329,]  0.32567871
[330,]  1.24149945
[331,] -0.26044656
[332,]  1.51624567
[333,]  0.23409663
[334,]  0.98506964
[335,]  1.14991738
[336,]  0.30736229
[337,]  1.46129643
[338,]  0.52715927
[339,]  0.32567871
[340,]  2.17563660
[341,] -0.07728242
[342,]  1.04001889
[343,]  1.25981586
[344,]  1.14991738
attr(,"scaled:center")
[1] 43.92193
attr(,"scaled:scale")
[1] 5.459584

$bill_depth_mm
              [,1]
  [1,]  0.78430007
  [2,]  0.12600328
  [3,]  0.42983257
  [4,]          NA
  [5,]  1.08812936
  [6,]  1.74642615
  [7,]  0.32855614
  [8,]  1.24004400
  [9,]  0.48047078
 [10,]  1.54387329
 [11,] -0.02591137
 [12,]  0.07536506
 [13,]  0.22727971
 [14,]  2.05025544
 [15,]  1.99961722
 [16,]  0.32855614
 [17,]  0.93621471
 [18,]  1.79706436
 [19,]  0.63238542
 [20,]  2.20217008
 [21,]  0.58174721
 [22,]  0.78430007
 [23,]  1.03749114
 [24,]  0.48047078
 [25,]  0.02472685
 [26,]  0.88557650
 [27,]  0.73366185
 [28,]  0.37919435
 [29,]  0.73366185
 [30,]  0.88557650
 [31,] -0.22846423
 [32,]  0.48047078
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 [35,] -0.07654958
 [36,]  1.99961722
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 [38,]  0.68302364
 [39,]  1.08812936
 [40,]  0.98685293
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 [42,]  0.63238542
 [43,]  0.68302364
 [44,]  1.29068222
 [45,] -0.12718780
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 [47,]  0.93621471
 [48,]  0.88557650
 [49,]  0.37919435
 [50,]  2.05025544
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 [54,]  1.18940579
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 [80,]  0.98685293
 [81,]  0.02472685
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 [84,]  1.13876757
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[100,]  0.68302364
[101,]  0.37919435
[102,]  1.44259686
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[104,]  1.44259686
[105,]  0.73366185
[106,]  0.88557650
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[108,]  1.44259686
[109,] -0.07654958
[110,]  0.93621471
[111,] -0.32974066
[112,]  1.59451151
[113,]  0.27791792
[114,]  1.18940579
[115,]  1.79706436
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[117,] -0.07654958
[118,]  1.69578793
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[120,]  0.73366185
[121,]  0.02472685
[122,]  1.34132043
[123,] -0.07654958
[124,]  0.68302364
[125,] -0.63356994
[126,]  0.93621471
[127,]  0.22727971
[128,]  0.58174721
[129,] -0.02591137
[130,]  0.42983257
[131,]  0.37919435
[132,]  1.03749114
[133,]  0.68302364
[134,]  0.68302364
[135,]  0.22727971
[136,]  0.17664149
[137,]  0.17664149
[138,]  1.49323508
[139,] -0.32974066
[140,]  0.37919435
[141,] -0.02591137
[142,]  0.02472685
[143,] -0.83612280
[144,] -0.07654958
[145,] -0.17782601
[146,]  0.78430007
[147,]  0.73366185
[148,]  0.63238542
[149,]  0.32855614
[150,]  0.48047078
[151,] -0.02591137
[152,]  0.68302364
[153,] -2.00080174
[154,] -0.43101709
[155,] -1.54505781
[156,] -0.98803745
[157,] -1.34250495
[158,] -1.84888710
[159,] -1.29186674
[160,] -0.93739923
[161,] -1.89952531
[162,] -0.88676102
[163,] -1.74761067
[164,] -0.53229351
[165,] -1.74761067
[166,] -1.29186674
[167,] -1.29186674
[168,] -0.73484637
[169,] -1.84888710
[170,] -0.98803745
[171,] -1.34250495
[172,] -1.03867566
[173,] -1.44378138
[174,] -1.34250495
[175,] -1.34250495
[176,] -0.68420816
[177,] -2.05143996
[178,] -1.03867566
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[180,] -1.08931388
[181,] -1.44378138
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[184,] -1.49441960
[185,] -1.34250495
[186,] -0.07654958
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[189,] -1.74761067
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[194,] -0.58293173
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[196,] -1.08931388
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[198,] -1.64633424
[199,] -1.64633424
[200,] -0.63356994
[201,] -1.95016353
[202,] -0.68420816
[203,] -1.49441960
[204,] -1.54505781
[205,] -1.39314317
[206,] -1.08931388
[207,] -1.39314317
[208,] -0.88676102
[209,] -1.64633424
[210,] -1.08931388
[211,] -1.34250495
[212,] -0.93739923
[213,] -1.69697245
[214,] -1.13995209
[215,] -1.64633424
[216,] -0.73484637
[217,] -1.49441960
[218,] -0.17782601
[219,] -1.39314317
[220,] -0.48165530
[221,] -1.49441960
[222,] -1.08931388
[223,] -1.08931388
[224,] -0.78548459
[225,] -0.78548459
[226,] -1.19059031
[227,] -1.08931388
[228,] -0.58293173
[229,] -1.49441960
[230,] -0.43101709
[231,] -1.69697245
[232,] -0.38037887
[233,] -1.34250495
[234,] -0.78548459
[235,] -1.29186674
[236,] -0.63356994
[237,] -1.69697245
[238,]  0.07536506
[239,] -1.39314317
[240,] -1.49441960
[241,] -1.59569603
[242,] -0.07654958
[243,] -1.08931388
[244,] -0.02591137
[245,] -1.34250495
[246,] -0.53229351
[247,] -1.24122852
[248,] -0.73484637
[249,] -0.68420816
[250,] -1.29186674
[251,] -1.39314317
[252,] -0.32974066
[253,] -1.08931388
[254,] -0.07654958
[255,] -0.83612280
[256,] -1.08931388
[257,] -1.69697245
[258,] -0.53229351
[259,] -1.24122852
[260,] -0.68420816
[261,] -1.59569603
[262,] -1.03867566
[263,] -0.98803745
[264,] -0.63356994
[265,] -0.98803745
[266,] -0.43101709
[267,] -1.54505781
[268,] -0.58293173
[269,] -0.73484637
[270,] -0.48165530
[271,] -1.74761067
[272,]          NA
[273,] -1.44378138
[274,] -0.73484637
[275,] -1.19059031
[276,] -0.53229351
[277,]  0.37919435
[278,]  1.18940579
[279,]  1.03749114
[280,]  0.78430007
[281,]  1.34132043
[282,]  0.32855614
[283,]  0.53110900
[284,]  0.53110900
[285,]  0.88557650
[286,]  1.39195865
[287,]  0.32855614
[288,]  1.59451151
[289,]  0.07536506
[290,]  0.48047078
[291,] -0.02591137
[292,]  1.24004400
[293,]  1.44259686
[294,]  0.32855614
[295,]  0.73366185
[296,]  0.53110900
[297,]  0.07536506
[298,]  0.17664149
[299,] -0.27910244
[300,]  1.13876757
[301,]  0.37919435
[302,]  0.93621471
[303,]  0.63238542
[304,]  0.93621471
[305,]  0.32855614
[306,]  1.44259686
[307,] -0.27910244
[308,]  1.84770258
[309,] -0.22846423
[310,]  0.83493828
[311,]  0.73366185
[312,] -0.17782601
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[314,]  1.79706436
[315,] -0.27910244
[316,]  1.39195865
[317,]  1.18940579
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[319,]  0.98685293
[320,] -0.07654958
[321,]  0.37919435
[322,]  0.68302364
[323,]  0.37919435
[324,]  1.24004400
[325,]  0.78430007
[326,]  0.07536506
[327,] -0.38037887
[328,]  0.93621471
[329,]  0.07536506
[330,]  1.29068222
[331,]  0.07536506
[332,]  0.83493828
[333,] -0.27910244
[334,]  1.39195865
[335,]  0.83493828
[336,]  1.13876757
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[338,] -0.32974066
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[340,]  1.34132043
[341,]  0.48047078
[342,]  0.53110900
[343,]  0.93621471
[344,]  0.78430007
attr(,"scaled:center")
[1] 17.15117
attr(,"scaled:scale")
[1] 1.974793

$flipper_length_mm
               [,1]
  [1,] -1.416271525
  [2,] -1.060696087
  [3,] -0.420660299
  [4,]           NA
  [5,] -0.562890474
  [6,] -0.776235737
  [7,] -1.416271525
  [8,] -0.420660299
  [9,] -0.562890474
 [10,] -0.776235737
 [11,] -1.060696087
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 [15,] -0.207315036
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 [24,] -1.131811175
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 [48,] -1.558501700
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 [51,] -1.060696087
 [52,] -0.918465912
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 [70,] -0.207315036
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 [76,] -0.420660299
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 [93,] -1.131811175
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[100,] -0.634005562
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[102,]  0.148260402
[103,] -1.274041350
[104,] -0.776235737
[105,] -0.562890474
[106,] -1.202926262
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attr(,"scaled:center")
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attr(,"scaled:scale")
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$sex
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[101] female male   female male   female male   female male   female male  
[111] female male   female male   female male   female male   female male  
[121] female male   female male   female male   female male   female male  
[131] female male   female male   female male   female male   female male  
[141] female male   female male   female male   male   female female male  
[151] female male   female male   female male   male   female female male  
[161] female male   female male   female male   female male   female male  
[171] female male   male   female female male   female male   <NA>   male  
[181] female male   male   female female male   female male   female male  
[191] female male   female male   female male   male   female female male  
[201] female male   female male   female male   female male   female male  
[211] female male   female male   female male   female male   <NA>   male  
[221] female male   female male   male   female female male   female male  
[231] female male   female male   female male   female male   female male  
[241] female male   female male   female male   female male   male   female
[251] female male   female male   female male   <NA>   male   female male  
[261] female male   female male   female male   female male   <NA>   male  
[271] female <NA>   female male   female male   female male   male   female
[281] male   female female male   female male   female male   female male  
[291] female male   male   female female male   female male   female male  
[301] female male   female male   female male   female male   female male  
[311] male   female female male   female male   male   female male   female
[321] female male   female male   male   female female male   female male  
[331] female male   female male   male   female male   female female male  
[341] female male   male   female
Levels: female male

$year
              [,1]
  [1,] -1.25748435
  [2,] -1.25748435
  [3,] -1.25748435
  [4,] -1.25748435
  [5,] -1.25748435
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 [29,] -1.25748435
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 [41,] -1.25748435
 [42,] -1.25748435
 [43,] -1.25748435
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 [46,] -1.25748435
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 [50,] -1.25748435
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 [60,] -0.03552216
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 [62,] -0.03552216
 [63,] -0.03552216
 [64,] -0.03552216
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 [67,] -0.03552216
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 [71,] -0.03552216
 [72,] -0.03552216
 [73,] -0.03552216
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 [76,] -0.03552216
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 [79,] -0.03552216
 [80,] -0.03552216
 [81,] -0.03552216
 [82,] -0.03552216
 [83,] -0.03552216
 [84,] -0.03552216
 [85,] -0.03552216
 [86,] -0.03552216
 [87,] -0.03552216
 [88,] -0.03552216
 [89,] -0.03552216
 [90,] -0.03552216
 [91,] -0.03552216
 [92,] -0.03552216
 [93,] -0.03552216
 [94,] -0.03552216
 [95,] -0.03552216
 [96,] -0.03552216
 [97,] -0.03552216
 [98,] -0.03552216
 [99,] -0.03552216
[100,] -0.03552216
[101,]  1.18644003
[102,]  1.18644003
[103,]  1.18644003
[104,]  1.18644003
[105,]  1.18644003
[106,]  1.18644003
[107,]  1.18644003
[108,]  1.18644003
[109,]  1.18644003
[110,]  1.18644003
[111,]  1.18644003
[112,]  1.18644003
[113,]  1.18644003
[114,]  1.18644003
[115,]  1.18644003
[116,]  1.18644003
[117,]  1.18644003
[118,]  1.18644003
[119,]  1.18644003
[120,]  1.18644003
[121,]  1.18644003
[122,]  1.18644003
[123,]  1.18644003
[124,]  1.18644003
[125,]  1.18644003
[126,]  1.18644003
[127,]  1.18644003
[128,]  1.18644003
[129,]  1.18644003
[130,]  1.18644003
[131,]  1.18644003
[132,]  1.18644003
[133,]  1.18644003
[134,]  1.18644003
[135,]  1.18644003
[136,]  1.18644003
[137,]  1.18644003
[138,]  1.18644003
[139,]  1.18644003
[140,]  1.18644003
[141,]  1.18644003
[142,]  1.18644003
[143,]  1.18644003
[144,]  1.18644003
[145,]  1.18644003
[146,]  1.18644003
[147,]  1.18644003
[148,]  1.18644003
[149,]  1.18644003
[150,]  1.18644003
[151,]  1.18644003
[152,]  1.18644003
[153,] -1.25748435
[154,] -1.25748435
[155,] -1.25748435
[156,] -1.25748435
[157,] -1.25748435
[158,] -1.25748435
[159,] -1.25748435
[160,] -1.25748435
[161,] -1.25748435
[162,] -1.25748435
[163,] -1.25748435
[164,] -1.25748435
[165,] -1.25748435
[166,] -1.25748435
[167,] -1.25748435
[168,] -1.25748435
[169,] -1.25748435
[170,] -1.25748435
[171,] -1.25748435
[172,] -1.25748435
[173,] -1.25748435
[174,] -1.25748435
[175,] -1.25748435
[176,] -1.25748435
[177,] -1.25748435
[178,] -1.25748435
[179,] -1.25748435
[180,] -1.25748435
[181,] -1.25748435
[182,] -1.25748435
[183,] -1.25748435
[184,] -1.25748435
[185,] -1.25748435
[186,] -1.25748435
[187,] -0.03552216
[188,] -0.03552216
[189,] -0.03552216
[190,] -0.03552216
[191,] -0.03552216
[192,] -0.03552216
[193,] -0.03552216
[194,] -0.03552216
[195,] -0.03552216
[196,] -0.03552216
[197,] -0.03552216
[198,] -0.03552216
[199,] -0.03552216
[200,] -0.03552216
[201,] -0.03552216
[202,] -0.03552216
[203,] -0.03552216
[204,] -0.03552216
[205,] -0.03552216
[206,] -0.03552216
[207,] -0.03552216
[208,] -0.03552216
[209,] -0.03552216
[210,] -0.03552216
[211,] -0.03552216
[212,] -0.03552216
[213,] -0.03552216
[214,] -0.03552216
[215,] -0.03552216
[216,] -0.03552216
[217,] -0.03552216
[218,] -0.03552216
[219,] -0.03552216
[220,] -0.03552216
[221,] -0.03552216
[222,] -0.03552216
[223,] -0.03552216
[224,] -0.03552216
[225,] -0.03552216
[226,] -0.03552216
[227,] -0.03552216
[228,] -0.03552216
[229,] -0.03552216
[230,] -0.03552216
[231,] -0.03552216
[232,] -0.03552216
[233,]  1.18644003
[234,]  1.18644003
[235,]  1.18644003
[236,]  1.18644003
[237,]  1.18644003
[238,]  1.18644003
[239,]  1.18644003
[240,]  1.18644003
[241,]  1.18644003
[242,]  1.18644003
[243,]  1.18644003
[244,]  1.18644003
[245,]  1.18644003
[246,]  1.18644003
[247,]  1.18644003
[248,]  1.18644003
[249,]  1.18644003
[250,]  1.18644003
[251,]  1.18644003
[252,]  1.18644003
[253,]  1.18644003
[254,]  1.18644003
[255,]  1.18644003
[256,]  1.18644003
[257,]  1.18644003
[258,]  1.18644003
[259,]  1.18644003
[260,]  1.18644003
[261,]  1.18644003
[262,]  1.18644003
[263,]  1.18644003
[264,]  1.18644003
[265,]  1.18644003
[266,]  1.18644003
[267,]  1.18644003
[268,]  1.18644003
[269,]  1.18644003
[270,]  1.18644003
[271,]  1.18644003
[272,]  1.18644003
[273,]  1.18644003
[274,]  1.18644003
[275,]  1.18644003
[276,]  1.18644003
[277,] -1.25748435
[278,] -1.25748435
[279,] -1.25748435
[280,] -1.25748435
[281,] -1.25748435
[282,] -1.25748435
[283,] -1.25748435
[284,] -1.25748435
[285,] -1.25748435
[286,] -1.25748435
[287,] -1.25748435
[288,] -1.25748435
[289,] -1.25748435
[290,] -1.25748435
[291,] -1.25748435
[292,] -1.25748435
[293,] -1.25748435
[294,] -1.25748435
[295,] -1.25748435
[296,] -1.25748435
[297,] -1.25748435
[298,] -1.25748435
[299,] -1.25748435
[300,] -1.25748435
[301,] -1.25748435
[302,] -1.25748435
[303,] -0.03552216
[304,] -0.03552216
[305,] -0.03552216
[306,] -0.03552216
[307,] -0.03552216
[308,] -0.03552216
[309,] -0.03552216
[310,] -0.03552216
[311,] -0.03552216
[312,] -0.03552216
[313,] -0.03552216
[314,] -0.03552216
[315,] -0.03552216
[316,] -0.03552216
[317,] -0.03552216
[318,] -0.03552216
[319,] -0.03552216
[320,] -0.03552216
[321,]  1.18644003
[322,]  1.18644003
[323,]  1.18644003
[324,]  1.18644003
[325,]  1.18644003
[326,]  1.18644003
[327,]  1.18644003
[328,]  1.18644003
[329,]  1.18644003
[330,]  1.18644003
[331,]  1.18644003
[332,]  1.18644003
[333,]  1.18644003
[334,]  1.18644003
[335,]  1.18644003
[336,]  1.18644003
[337,]  1.18644003
[338,]  1.18644003
[339,]  1.18644003
[340,]  1.18644003
[341,]  1.18644003
[342,]  1.18644003
[343,]  1.18644003
[344,]  1.18644003
attr(,"scaled:center")
[1] 2008.029
attr(,"scaled:scale")
[1] 0.8183559

Image source: https://bookdown.org/hneth/ds4psy

PA 8: The 12 Days of Christmas

Image source: https://studioplayhouse.org/the-12-days-of-christmas/

A (boring) Example

Timothee Chalamet’s Statistics

chalamet_statistics <- function(n){
  lyrics <- map_chr(1:n, ~ "Statistics, yup")
  
  return(
    glue::glue("{lyrics}")
    )
}
n = 10
map_chr(1:n, ~ "Statistics, yup")
 [1] "Statistics, yup" "Statistics, yup" "Statistics, yup" "Statistics, yup"
 [5] "Statistics, yup" "Statistics, yup" "Statistics, yup" "Statistics, yup"
 [9] "Statistics, yup" "Statistics, yup"

glue – “interprets string literals that are small, fast, and dependency-free”

glue does this by embedding R expressions in curly braces which are then evaluated and inserted into the argument string.

library(glue)
name <- "Emily"
glue('My name is {name}.')
My name is Emily.
chalamet_statistics <- function(n){
  lyrics <- map_chr(1:n, ~ "Statistics, yup")
  
  return(
    glue::glue("{lyrics}")
    )
}

chalamet_statistics(n = 3)
Statistics, yup
Statistics, yup
Statistics, yup

Long strings are broken by line and concatenated together.

A (better) Example – 99 Bottles of Beer

99 bottles of beer on the wall, 99 bottles of beer. Take one down, pass it around, 98 bottles of beer on the wall…

bottles_lyrics <- function(n){
  lyrics <- glue::glue("{n} bottles of beer on the wall, {n} bottles of beer \nTake one down, pass it around, {n -1} bottles of beer on the wall")
  return(lyrics)
}

bottles_lyrics(3)
3 bottles of beer on the wall, 3 bottles of beer 
Take one down, pass it around, 2 bottles of beer on the wall
bottles_song <- function(n){
  song <- map_chr(n:0, bottles_lyrics)
  return(
    glue::glue("{song}")
  )
}

bottles_song(3)
3 bottles of beer on the wall, 3 bottles of beer 
Take one down, pass it around, 2 bottles of beer on the wall
2 bottles of beer on the wall, 2 bottles of beer 
Take one down, pass it around, 1 bottles of beer on the wall
1 bottles of beer on the wall, 1 bottles of beer 
Take one down, pass it around, 0 bottles of beer on the wall
0 bottles of beer on the wall, 0 bottles of beer 
Take one down, pass it around, -1 bottles of beer on the wall

No more bottles of beer on the wall, no more bottles of beer.

Go to the store and buy some more, 99 bottles of beer on the wall…

bottles_lyrics <- function(n){
  if(n == 0){
    lyrics <- glue::glue("No more bottles of beer on the wall, no more bottles of beer. \nGo to the store and buy some more, 99 bottles of beer on the wall")
  } else{
  lyrics <- glue::glue("{n} bottles of beer on the wall, {n} bottles of beer \nTake one down, pass it around, {n -1} bottles of beer on the wall")
  }
  return(lyrics)
}
bottles_song(4)
4 bottles of beer on the wall, 4 bottles of beer 
Take one down, pass it around, 3 bottles of beer on the wall
3 bottles of beer on the wall, 3 bottles of beer 
Take one down, pass it around, 2 bottles of beer on the wall
2 bottles of beer on the wall, 2 bottles of beer 
Take one down, pass it around, 1 bottles of beer on the wall
1 bottles of beer on the wall, 1 bottles of beer 
Take one down, pass it around, 0 bottles of beer on the wall
No more bottles of beer on the wall, no more bottles of beer. 
Go to the store and buy some more, 99 bottles of beer on the wall

To do…

  • PA 8: The 12 Days of Christmas Starter Functions
    • Due Wednesday 3/1 at 8am
  • Project Proposal – Data
    • Due Sunday 3/5 at 11:59pm

Wednesday, March 1st

Today we will…

  • Lab 7 Comments
  • Clarify case_when()
  • Recap pmap()
  • Preview for Lab 8: Writing the Full Song
  • Work-time
    • Lab and Challenge 8
    • Project Proposal

Comments from Lab 7: Functions and Fish

Get creative with your plots…

  • Rotate your axes
  • Make sure people don’t tilt their head
  • use non-default colors
  • change font and/or font size
  • add legend to your title!
labs(title = str_c("Missing Fish Weights for ",
      "<span style = 'color:#A6CEE3'>**Trip 1**</span>",
      " and <span style = 'color:#1F87B4'>**Trip 2**</span>"),
      subtitle = "by Section of Blackfoot River"
    )

How did I figure out the colors?

library(RColorBrewer)
brewer.pal(n = 2, name = "Paired")
[1] "#A6CEE3" "#1F78B4" "#B2DF8A"

dplyr 1.1.0 – case_when(.default = )

new_var <- case_when(condition1 ~ change1,
                     condition2 ~ change2,
                     .default ~ variable
                     )
  • The condition should result in a TRUE/FALSE Boolean value
  • The change is some sort of modification to a variable

Tip: Test it out on a simple case, then generalize!

Works with older versions of dplyr – will always work!

pos_neg_zero <- function(x){
  stopifnot(is.numeric(x))
  
  state <- case_when(x > 0 ~ "Greater than 0!", 
                     x < 0 ~ "Less than 0!", 
                     TRUE ~ "Equal to 0!")
  return(state)
}

x <- seq(from = -4, to = 4, by = 1)
pos_neg_zero(x)
[1] "Less than 0!"    "Less than 0!"    "Less than 0!"    "Less than 0!"   
[5] "Equal to 0!"     "Greater than 0!" "Greater than 0!" "Greater than 0!"
[9] "Greater than 0!"

case_when(.default = )

Works with dplyr versions 1.1.0 over newer.

pos_neg_zero <- function(x){
  stopifnot(is.numeric(x))
  
  state <- case_when(x > 0 ~ "Greater than 0!", 
                     x < 0 ~ "Less than 0!", 
                     .default = "Equal to 0!")
  return(state)
}

x <- seq(from = -4, to = 4, by = 1)
pos_neg_zero(x)
[1] "Less than 0!"    "Less than 0!"    "Less than 0!"    "Less than 0!"   
[5] "Equal to 0!"     "Greater than 0!" "Greater than 0!" "Greater than 0!"
[9] "Greater than 0!"

What is pmap() doing?

Lab and Challenge: 12 Days of Christmas

  1. Write a sing_day() function
  • what does “one day” lyrics look like
  • what order do “one day” lyrics go in

Tip

The function glue_collapse() might be useful – look at the arguments in the R documentation.

  1. Iterate your sing_day() function over a set of days

  2. Style your output!

To do…

  • Lab 8: The 12 Days of Christmas Sing the Full Song
    • Due Friday 3/3 at 11:59pm
  • Challenge 8: Styling Your Song
    • Due Saturday 3/4 at 11:59pm
  • Read Chapter 9: Regression & Simulation
    • Check-in 9.1 & 9.2 due Monday 3/6 at 8am